Interpretive Summary: Undercooked chicken products are commonly implicated as transmission vehicles in food poisoning outbreaks caused by Salmonella. Thus, there has been a need to better define the heat treatment given to these foods to provide an adequate degree of protection against survival of this pathogen. We developed a mathematical model for predicting the destruction of Salmonella in chicken with heating temperature and two natural antimicrobials, carvacrol and cinnamaldehyde, as controlling factors. The model can be used to predict the time required at any temperature to kill a specific number of this deadly bacterium. The results will be of immediate use to consumers and to the food industry and regulatory agencies to aid in ensuring the safety of our food supply.

Technical Abstract:
We investigated the combined effect of three internal temperatures (60, 65 and 71.1C) and four concentrations (0.0, 0.1, 0.5 and 1% vol/wt) of two natural antimicrobials on the heat resistance of an eight-strain cocktail of Salmonella serovars in chicken meat. A complete factorial design (3 x 4 x 4) was used to assess the effects and interactions of heating temperature, and the two antimicrobials, carvacrol and cinnamaldehyde. The 48 variable combinations were replicated to provide a total of 96 survivor curves from the experimental data. Mathematical models were then developed to quantify the combined effect of these parameters on heat resistance of starved Salmonella cells. The theoretical analysis shows that the addition of plant-derived antimicrobials overcomes the heat resistance of starvation-stressed Salmonella in ground chicken meat. The influence of the antimicrobials allows reduced heat treatments, thus reducing heat-induced damage to the nutritional quality of ground-chicken products. Of the models developed, although the omnibus log-linear model with tail and the omnibus sigmoid model could represent equally well the experimental survivor curves, their discrepancy only became apparent when lethality times (D-values and 7.0 log lethality) from each of the models were calculated. Given the concave nature of the inactivation curves, the log-linear model with tail greatly underestimates the times needed to obtain 7.0 log lethality. Thus, a polynomial secondary model, based on the sigmoid model, was developed for accurately predicting the 7.0-log reduction times. The three-factor predictive model can be used to estimate the processing times and temperatures required for achieving specific log reductions of the pathogen, including the regulatory recommendation of 7.0-log reduction of Salmonella in ground chicken.